Overview

Dataset statistics

Number of variables18
Number of observations2014
Missing cells78
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory363.5 KiB
Average record size in memory184.8 B

Variable types

Categorical2
Numeric16

Alerts

Symbol has constant value "RELIANCE"Constant
Series has constant value "EQ"Constant
Prev Close is highly overall correlated with Open and 10 other fieldsHigh correlation
Open is highly overall correlated with Prev Close and 10 other fieldsHigh correlation
High is highly overall correlated with Prev Close and 10 other fieldsHigh correlation
Low is highly overall correlated with Prev Close and 10 other fieldsHigh correlation
Last is highly overall correlated with Prev Close and 10 other fieldsHigh correlation
Close is highly overall correlated with Prev Close and 10 other fieldsHigh correlation
VWAP is highly overall correlated with Prev Close and 10 other fieldsHigh correlation
Volume is highly overall correlated with Turnover and 2 other fieldsHigh correlation
Turnover is highly overall correlated with Prev Close and 12 other fieldsHigh correlation
Trades is highly overall correlated with Prev Close and 12 other fieldsHigh correlation
Deliverable Volume is highly overall correlated with Volume and 2 other fieldsHigh correlation
MA for 10 days is highly overall correlated with Prev Close and 10 other fieldsHigh correlation
MA for 20 days is highly overall correlated with Prev Close and 10 other fieldsHigh correlation
MA for 50 days is highly overall correlated with Prev Close and 10 other fieldsHigh correlation
MA for 50 days has 49 (2.4%) missing valuesMissing
Volume has unique valuesUnique
Turnover has unique valuesUnique
Deliverable Volume has unique valuesUnique

Reproduction

Analysis started2023-03-15 12:42:05.618418
Analysis finished2023-03-15 12:43:13.142435
Duration1 minute and 7.52 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Symbol
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.0 KiB
RELIANCE
2014 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16112
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRELIANCE
2nd rowRELIANCE
3rd rowRELIANCE
4th rowRELIANCE
5th rowRELIANCE

Common Values

ValueCountFrequency (%)
RELIANCE 2014
100.0%

Length

2023-03-15T12:43:13.240850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-15T12:43:13.387781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
reliance 2014
100.0%

Most occurring characters

ValueCountFrequency (%)
E 4028
25.0%
R 2014
12.5%
L 2014
12.5%
I 2014
12.5%
A 2014
12.5%
N 2014
12.5%
C 2014
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16112
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 4028
25.0%
R 2014
12.5%
L 2014
12.5%
I 2014
12.5%
A 2014
12.5%
N 2014
12.5%
C 2014
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 16112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 4028
25.0%
R 2014
12.5%
L 2014
12.5%
I 2014
12.5%
A 2014
12.5%
N 2014
12.5%
C 2014
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 4028
25.0%
R 2014
12.5%
L 2014
12.5%
I 2014
12.5%
A 2014
12.5%
N 2014
12.5%
C 2014
12.5%

Series
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.0 KiB
EQ
2014 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4028
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEQ
2nd rowEQ
3rd rowEQ
4th rowEQ
5th rowEQ

Common Values

ValueCountFrequency (%)
EQ 2014
100.0%

Length

2023-03-15T12:43:13.502512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-15T12:43:13.636828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
eq 2014
100.0%

Most occurring characters

ValueCountFrequency (%)
E 2014
50.0%
Q 2014
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4028
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2014
50.0%
Q 2014
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4028
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2014
50.0%
Q 2014
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2014
50.0%
Q 2014
50.0%

Prev Close
Real number (ℝ)

Distinct1938
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1510.4711
Minimum780.9
Maximum2819.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:13.774279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum780.9
5-th percentile876.6775
Q1996.1125
median1281.6
Q32077.075
95-th percentile2590.385
Maximum2819.85
Range2038.95
Interquartile range (IQR)1080.9625

Descriptive statistics

Standard deviation600.61964
Coefficient of variation (CV)0.39763729
Kurtosis-1.0655732
Mean1510.4711
Median Absolute Deviation (MAD)334.925
Skewness0.66054828
Sum3042088.8
Variance360743.96
MonotonicityNot monotonic
2023-03-15T12:43:13.945207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
978.95 4
 
0.2%
2619.05 2
 
0.1%
1964.05 2
 
0.1%
1089.4 2
 
0.1%
2577.8 2
 
0.1%
1096.35 2
 
0.1%
2606.6 2
 
0.1%
904.35 2
 
0.1%
950.35 2
 
0.1%
2479.85 2
 
0.1%
Other values (1928) 1992
98.9%
ValueCountFrequency (%)
780.9 1
< 0.1%
786.45 1
< 0.1%
798.35 1
< 0.1%
799.15 1
< 0.1%
810.75 1
< 0.1%
811.7 1
< 0.1%
816.9 1
< 0.1%
817.4 1
< 0.1%
817.9 1
< 0.1%
818.1 1
< 0.1%
ValueCountFrequency (%)
2819.85 1
< 0.1%
2798.75 1
< 0.1%
2790.25 1
< 0.1%
2782.1 1
< 0.1%
2780.45 1
< 0.1%
2779.5 1
< 0.1%
2778.35 1
< 0.1%
2775.65 1
< 0.1%
2772.75 1
< 0.1%
2767.55 1
< 0.1%

Open
Real number (ℝ)

Distinct1726
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1512.3259
Minimum791.75
Maximum2856.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:14.116095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum791.75
5-th percentile878.2925
Q1995.925
median1285.05
Q32080.75
95-th percentile2588.0875
Maximum2856.15
Range2064.4
Interquartile range (IQR)1084.825

Descriptive statistics

Standard deviation601.39451
Coefficient of variation (CV)0.39766197
Kurtosis-1.0741362
Mean1512.3259
Median Absolute Deviation (MAD)338.575
Skewness0.65663571
Sum3045824.4
Variance361675.35
MonotonicityNot monotonic
2023-03-15T12:43:14.285316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
950 7
 
0.3%
2085 5
 
0.2%
2600 5
 
0.2%
1375 4
 
0.2%
1006 4
 
0.2%
880 4
 
0.2%
930 4
 
0.2%
1095 4
 
0.2%
1028 4
 
0.2%
2610 4
 
0.2%
Other values (1716) 1969
97.8%
ValueCountFrequency (%)
791.75 1
< 0.1%
795 1
< 0.1%
799.2 1
< 0.1%
799.65 1
< 0.1%
815 1
< 0.1%
816.6 1
< 0.1%
818 1
< 0.1%
820 1
< 0.1%
821.3 1
< 0.1%
821.7 1
< 0.1%
ValueCountFrequency (%)
2856.15 1
< 0.1%
2809.95 1
< 0.1%
2785 1
< 0.1%
2780 1
< 0.1%
2772.75 1
< 0.1%
2771.9 1
< 0.1%
2769.9 1
< 0.1%
2762 1
< 0.1%
2758.9 1
< 0.1%
2755.85 1
< 0.1%

High
Real number (ℝ)

Distinct1831
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1529.2465
Minimum793.4
Maximum2856.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:14.453372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum793.4
5-th percentile889.79
Q11006.475
median1296.975
Q32100.3625
95-th percentile2610.895
Maximum2856.15
Range2062.75
Interquartile range (IQR)1093.8875

Descriptive statistics

Standard deviation607.59265
Coefficient of variation (CV)0.39731504
Kurtosis-1.07548
Mean1529.2465
Median Absolute Deviation (MAD)339.5
Skewness0.6550909
Sum3079902.5
Variance369168.83
MonotonicityNot monotonic
2023-03-15T12:43:14.616714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 5
 
0.2%
982 4
 
0.2%
1550 4
 
0.2%
1015 4
 
0.2%
905 3
 
0.1%
952.8 3
 
0.1%
926 3
 
0.1%
1281 3
 
0.1%
1009 3
 
0.1%
1327 3
 
0.1%
Other values (1821) 1979
98.3%
ValueCountFrequency (%)
793.4 1
< 0.1%
802.9 1
< 0.1%
803.85 1
< 0.1%
817.7 1
< 0.1%
823.25 1
< 0.1%
824.95 1
< 0.1%
825 1
< 0.1%
825.7 1
< 0.1%
826.85 1
< 0.1%
827.05 1
< 0.1%
ValueCountFrequency (%)
2856.15 1
< 0.1%
2851 1
< 0.1%
2828 1
< 0.1%
2817.35 1
< 0.1%
2814 1
< 0.1%
2805.5 1
< 0.1%
2803 1
< 0.1%
2802 1
< 0.1%
2795 1
< 0.1%
2791.1 1
< 0.1%

Low
Real number (ℝ)

Distinct1816
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1494.6506
Minimum779.1
Maximum2786.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:14.791500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum779.1
5-th percentile866.685
Q1986.5375
median1272.425
Q32046.3125
95-th percentile2566
Maximum2786.1
Range2007
Interquartile range (IQR)1059.775

Descriptive statistics

Standard deviation594.53419
Coefficient of variation (CV)0.3977747
Kurtosis-1.0666238
Mean1494.6506
Median Absolute Deviation (MAD)335.375
Skewness0.66001119
Sum3010226.2
Variance353470.9
MonotonicityNot monotonic
2023-03-15T12:43:14.966033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 5
 
0.2%
2495 4
 
0.2%
1325 4
 
0.2%
920 4
 
0.2%
990 4
 
0.2%
1047 4
 
0.2%
935 4
 
0.2%
2360 3
 
0.1%
1932 3
 
0.1%
978 3
 
0.1%
Other values (1806) 1976
98.1%
ValueCountFrequency (%)
779.1 1
< 0.1%
780.2 1
< 0.1%
785.35 1
< 0.1%
795.5 1
< 0.1%
795.6 1
< 0.1%
796.45 1
< 0.1%
801.45 1
< 0.1%
805.2 1
< 0.1%
812.1 1
< 0.1%
812.2 1
< 0.1%
ValueCountFrequency (%)
2786.1 1
< 0.1%
2777.3 1
< 0.1%
2758.05 1
< 0.1%
2755.05 1
< 0.1%
2752.05 1
< 0.1%
2751.8 1
< 0.1%
2744.2 1
< 0.1%
2742 1
< 0.1%
2732 1
< 0.1%
2716.3 1
< 0.1%

Last
Real number (ℝ)

Distinct1819
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1511.1901
Minimum781.2
Maximum2810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:15.140563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum781.2
5-th percentile877
Q1995.6
median1282
Q32077.575
95-th percentile2589
Maximum2810
Range2028.8
Interquartile range (IQR)1081.975

Descriptive statistics

Standard deviation600.76329
Coefficient of variation (CV)0.39754316
Kurtosis-1.0702134
Mean1511.1901
Median Absolute Deviation (MAD)336.025
Skewness0.65778603
Sum3043536.9
Variance360916.53
MonotonicityNot monotonic
2023-03-15T12:43:15.318849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
842 4
 
0.2%
2112 4
 
0.2%
1330 4
 
0.2%
895 4
 
0.2%
1040 3
 
0.1%
2178 3
 
0.1%
2525 3
 
0.1%
2668 3
 
0.1%
1089 3
 
0.1%
1288 3
 
0.1%
Other values (1809) 1980
98.3%
ValueCountFrequency (%)
781.2 1
< 0.1%
786.25 1
< 0.1%
798.4 1
< 0.1%
799 1
< 0.1%
810 1
< 0.1%
814.1 1
< 0.1%
816.6 1
< 0.1%
816.95 2
0.1%
819.2 1
< 0.1%
819.5 1
< 0.1%
ValueCountFrequency (%)
2810 1
< 0.1%
2799.7 1
< 0.1%
2796.05 1
< 0.1%
2788.2 1
< 0.1%
2782 1
< 0.1%
2780.9 1
< 0.1%
2778 1
< 0.1%
2776 1
< 0.1%
2767.05 1
< 0.1%
2766.25 1
< 0.1%

Close
Real number (ℝ)

Distinct1938
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1511.2361
Minimum780.9
Maximum2819.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:15.502524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum780.9
5-th percentile876.6775
Q1996.375
median1281.75
Q32077.225
95-th percentile2590.385
Maximum2819.85
Range2038.95
Interquartile range (IQR)1080.85

Descriptive statistics

Standard deviation600.81166
Coefficient of variation (CV)0.39756305
Kurtosis-1.0692263
Mean1511.2361
Median Absolute Deviation (MAD)335.075
Skewness0.65842999
Sum3043629.6
Variance360974.65
MonotonicityNot monotonic
2023-03-15T12:43:15.678997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
978.95 4
 
0.2%
1010.4 2
 
0.1%
904.35 2
 
0.1%
2577.8 2
 
0.1%
969.15 2
 
0.1%
876.45 2
 
0.1%
1109.4 2
 
0.1%
1089.4 2
 
0.1%
1096.35 2
 
0.1%
950.35 2
 
0.1%
Other values (1928) 1992
98.9%
ValueCountFrequency (%)
780.9 1
< 0.1%
786.45 1
< 0.1%
798.35 1
< 0.1%
799.15 1
< 0.1%
810.75 1
< 0.1%
811.7 1
< 0.1%
816.9 1
< 0.1%
817.4 1
< 0.1%
817.9 1
< 0.1%
818.1 1
< 0.1%
ValueCountFrequency (%)
2819.85 1
< 0.1%
2798.75 1
< 0.1%
2790.25 1
< 0.1%
2782.1 1
< 0.1%
2780.45 1
< 0.1%
2779.5 1
< 0.1%
2778.35 1
< 0.1%
2775.65 1
< 0.1%
2772.75 1
< 0.1%
2767.55 1
< 0.1%

VWAP
Real number (ℝ)

Distinct1995
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1512.2102
Minimum785.51
Maximum2823.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:15.872682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum785.51
5-th percentile878.187
Q1996.7325
median1284.395
Q32075.69
95-th percentile2586.1105
Maximum2823.91
Range2038.4
Interquartile range (IQR)1078.9575

Descriptive statistics

Standard deviation601.06692
Coefficient of variation (CV)0.39747578
Kurtosis-1.0714121
Mean1512.2102
Median Absolute Deviation (MAD)336.64
Skewness0.6574741
Sum3045591.3
Variance361281.44
MonotonicityNot monotonic
2023-03-15T12:43:16.054457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1024.42 2
 
0.1%
923.12 2
 
0.1%
981.35 2
 
0.1%
1240.68 2
 
0.1%
850.31 2
 
0.1%
1022.77 2
 
0.1%
941.25 2
 
0.1%
2540.02 2
 
0.1%
848.94 2
 
0.1%
916.3 2
 
0.1%
Other values (1985) 1994
99.0%
ValueCountFrequency (%)
785.51 1
< 0.1%
789.61 1
< 0.1%
797.81 1
< 0.1%
805.86 1
< 0.1%
807.78 1
< 0.1%
811.68 1
< 0.1%
815.7 1
< 0.1%
817.3 1
< 0.1%
818.09 1
< 0.1%
819.18 1
< 0.1%
ValueCountFrequency (%)
2823.91 1
< 0.1%
2817.63 1
< 0.1%
2794.98 1
< 0.1%
2793.34 1
< 0.1%
2783.29 1
< 0.1%
2775.79 1
< 0.1%
2773.48 1
< 0.1%
2773.03 1
< 0.1%
2768.55 1
< 0.1%
2763.87 1
< 0.1%

Volume
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2014
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7725705.9
Minimum299511
Maximum65230894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:16.237479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum299511
5-th percentile2094310.9
Q13756861
median5922311.5
Q39296069.5
95-th percentile19590671
Maximum65230894
Range64931383
Interquartile range (IQR)5539208.5

Descriptive statistics

Standard deviation6640182.1
Coefficient of variation (CV)0.85949196
Kurtosis16.674465
Mean7725705.9
Median Absolute Deviation (MAD)2521459
Skewness3.2509426
Sum1.5559572 × 1010
Variance4.4092018 × 1013
MonotonicityNot monotonic
2023-03-15T12:43:16.414413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
677439 1
 
< 0.1%
15784612 1
 
< 0.1%
17521196 1
 
< 0.1%
27781772 1
 
< 0.1%
48828852 1
 
< 0.1%
18677694 1
 
< 0.1%
19282519 1
 
< 0.1%
23623299 1
 
< 0.1%
24509162 1
 
< 0.1%
17790565 1
 
< 0.1%
Other values (2004) 2004
99.5%
ValueCountFrequency (%)
299511 1
< 0.1%
362170 1
< 0.1%
677439 1
< 0.1%
787160 1
< 0.1%
815433 1
< 0.1%
911410 1
< 0.1%
999364 1
< 0.1%
1127113 1
< 0.1%
1195257 1
< 0.1%
1238135 1
< 0.1%
ValueCountFrequency (%)
65230894 1
< 0.1%
64751766 1
< 0.1%
64458598 1
< 0.1%
61711388 1
< 0.1%
55656793 1
< 0.1%
48828852 1
< 0.1%
47923444 1
< 0.1%
46029119 1
< 0.1%
45857806 1
< 0.1%
42477225 1
< 0.1%

Turnover
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2014
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.238188 × 1015
Minimum2.7819327 × 1013
Maximum1.4734336 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:16.588767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.7819327 × 1013
5-th percentile2.0565337 × 1014
Q14.0965442 × 1014
median9.1359204 × 1014
Q31.5849121 × 1015
95-th percentile3.3837387 × 1015
Maximum1.4734336 × 1016
Range1.4706517 × 1016
Interquartile range (IQR)1.1752576 × 1015

Descriptive statistics

Standard deviation1.2615303 × 1015
Coefficient of variation (CV)1.018852
Kurtosis19.233525
Mean1.238188 × 1015
Median Absolute Deviation (MAD)5.4360752 × 1014
Skewness3.3779508
Sum2.4937106 × 1018
Variance1.5914588 × 1030
MonotonicityNot monotonic
2023-03-15T12:43:16.762232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.013058524 × 10131
 
< 0.1%
2.478556088 × 10151
 
< 0.1%
3.034431052 × 10151
 
< 0.1%
4.920665189 × 10151
 
< 0.1%
8.434432145 × 10151
 
< 0.1%
3.056409828 × 10151
 
< 0.1%
3.121151894 × 10151
 
< 0.1%
3.820298264 × 10151
 
< 0.1%
3.90527372 × 10151
 
< 0.1%
2.76230181 × 10151
 
< 0.1%
Other values (2004) 2004
99.5%
ValueCountFrequency (%)
2.781932654 × 10131
< 0.1%
3.812023602 × 10131
< 0.1%
6.013058524 × 10131
< 0.1%
9.051701818 × 10131
< 0.1%
1.083937776 × 10141
< 0.1%
1.130270449 × 10141
< 0.1%
1.219206576 × 10141
< 0.1%
1.254250672 × 10141
< 0.1%
1.257352825 × 10141
< 0.1%
1.270670776 × 10141
< 0.1%
ValueCountFrequency (%)
1.473433631 × 10161
< 0.1%
1.236079553 × 10161
< 0.1%
1.184668763 × 10161
< 0.1%
9.211999493 × 10151
< 0.1%
9.179980463 × 10151
< 0.1%
8.912049493 × 10151
< 0.1%
8.839332015 × 10151
< 0.1%
8.835029614 × 10151
< 0.1%
8.750594271 × 10151
< 0.1%
8.599389887 × 10151
< 0.1%

Trades
Real number (ℝ)

Distinct2008
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190194.53
Minimum8723
Maximum1428490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:16.948191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8723
5-th percentile56567.25
Q192076.5
median158245
Q3234462
95-th percentile456520.65
Maximum1428490
Range1419767
Interquartile range (IQR)142385.5

Descriptive statistics

Standard deviation142424.05
Coefficient of variation (CV)0.74883354
Kurtosis12.316083
Mean190194.53
Median Absolute Deviation (MAD)69664
Skewness2.6832379
Sum3.8305179 × 108
Variance2.0284609 × 1010
MonotonicityNot monotonic
2023-03-15T12:43:17.120031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92076 2
 
0.1%
91666 2
 
0.1%
63040 2
 
0.1%
169662 2
 
0.1%
219641 2
 
0.1%
116207 2
 
0.1%
467105 1
 
< 0.1%
424919 1
 
< 0.1%
299203 1
 
< 0.1%
327308 1
 
< 0.1%
Other values (1998) 1998
99.2%
ValueCountFrequency (%)
8723 1
< 0.1%
9214 1
< 0.1%
9771 1
< 0.1%
16263 1
< 0.1%
28170 1
< 0.1%
29486 1
< 0.1%
30701 1
< 0.1%
31269 1
< 0.1%
31291 1
< 0.1%
33745 1
< 0.1%
ValueCountFrequency (%)
1428490 1
< 0.1%
1285533 1
< 0.1%
1233053 1
< 0.1%
1194059 1
< 0.1%
1165095 1
< 0.1%
1154959 1
< 0.1%
1078097 1
< 0.1%
990935 1
< 0.1%
888904 1
< 0.1%
855344 1
< 0.1%

Deliverable Volume
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2014
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3347981.6
Minimum98030
Maximum19734107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:17.301576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum98030
5-th percentile1059303.9
Q11921533.8
median2779887.5
Q34091048.8
95-th percentile7775210.8
Maximum19734107
Range19636077
Interquartile range (IQR)2169515

Descriptive statistics

Standard deviation2267164.6
Coefficient of variation (CV)0.67717356
Kurtosis9.1557832
Mean3347981.6
Median Absolute Deviation (MAD)1022878
Skewness2.373249
Sum6.7428349 × 109
Variance5.1400353 × 1012
MonotonicityNot monotonic
2023-03-15T12:43:17.481572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
286377 1
 
< 0.1%
4083577 1
 
< 0.1%
2789706 1
 
< 0.1%
7287358 1
 
< 0.1%
15752717 1
 
< 0.1%
7786788 1
 
< 0.1%
7420780 1
 
< 0.1%
5774484 1
 
< 0.1%
5136354 1
 
< 0.1%
4514688 1
 
< 0.1%
Other values (2004) 2004
99.5%
ValueCountFrequency (%)
98030 1
< 0.1%
125224 1
< 0.1%
286377 1
< 0.1%
347306 1
< 0.1%
402932 1
< 0.1%
418584 1
< 0.1%
436975 1
< 0.1%
440625 1
< 0.1%
499595 1
< 0.1%
518886 1
< 0.1%
ValueCountFrequency (%)
19734107 1
< 0.1%
19195053 1
< 0.1%
19148342 1
< 0.1%
18435434 1
< 0.1%
17737691 1
< 0.1%
16920113 1
< 0.1%
16124097 1
< 0.1%
15752717 1
< 0.1%
15742153 1
< 0.1%
15175508 1
< 0.1%

%Deliverble
Real number (ℝ)

Distinct1615
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48103054
Minimum0.1148
Maximum0.827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:17.661116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1148
5-th percentile0.2602
Q10.399225
median0.49225
Q30.56685
95-th percentile0.67247
Maximum0.827
Range0.7122
Interquartile range (IQR)0.167625

Descriptive statistics

Standard deviation0.12358781
Coefficient of variation (CV)0.25692301
Kurtosis-0.23450004
Mean0.48103054
Median Absolute Deviation (MAD)0.0833
Skewness-0.22716141
Sum968.7955
Variance0.015273948
MonotonicityNot monotonic
2023-03-15T12:43:17.859430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5527 5
 
0.2%
0.5752 4
 
0.2%
0.529 4
 
0.2%
0.5715 4
 
0.2%
0.3873 4
 
0.2%
0.4576 4
 
0.2%
0.538 4
 
0.2%
0.4933 3
 
0.1%
0.4345 3
 
0.1%
0.5542 3
 
0.1%
Other values (1605) 1976
98.1%
ValueCountFrequency (%)
0.1148 1
< 0.1%
0.1291 1
< 0.1%
0.136 1
< 0.1%
0.142 1
< 0.1%
0.1479 1
< 0.1%
0.1508 1
< 0.1%
0.154 1
< 0.1%
0.1585 1
< 0.1%
0.1601 1
< 0.1%
0.1615 1
< 0.1%
ValueCountFrequency (%)
0.827 1
< 0.1%
0.8217 1
< 0.1%
0.8197 1
< 0.1%
0.8024 1
< 0.1%
0.7991 1
< 0.1%
0.7897 1
< 0.1%
0.7789 1
< 0.1%
0.7769 1
< 0.1%
0.7732 2
0.1%
0.7687 1
< 0.1%

MA for 10 days
Real number (ℝ)

Distinct1998
Distinct (%)99.7%
Missing9
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1510.7758
Minimum810.395
Maximum2759.255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:18.040934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum810.395
5-th percentile881.626
Q1998.695
median1278.31
Q32074.815
95-th percentile2589.118
Maximum2759.255
Range1948.86
Interquartile range (IQR)1076.12

Descriptive statistics

Standard deviation598.45637
Coefficient of variation (CV)0.3961252
Kurtosis-1.0753974
Mean1510.7758
Median Absolute Deviation (MAD)330.45
Skewness0.66268773
Sum3029105.5
Variance358150.03
MonotonicityNot monotonic
2023-03-15T12:43:18.808345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1283.88 2
 
0.1%
972.335 2
 
0.1%
922.235 2
 
0.1%
927.785 2
 
0.1%
1032.82 2
 
0.1%
1017.77 2
 
0.1%
836.635 2
 
0.1%
2079.5 1
 
< 0.1%
1524.805 1
 
< 0.1%
2109.65 1
 
< 0.1%
Other values (1988) 1988
98.7%
(Missing) 9
 
0.4%
ValueCountFrequency (%)
810.395 1
< 0.1%
810.825 1
< 0.1%
811.72 1
< 0.1%
813.125 1
< 0.1%
813.885 1
< 0.1%
815.1 1
< 0.1%
815.89 1
< 0.1%
819.82 1
< 0.1%
823.25 1
< 0.1%
825.91 1
< 0.1%
ValueCountFrequency (%)
2759.255 1
< 0.1%
2753.97 1
< 0.1%
2751.5 1
< 0.1%
2735.355 1
< 0.1%
2730.31 1
< 0.1%
2721.21 1
< 0.1%
2721.02 1
< 0.1%
2720.565 1
< 0.1%
2716.845 1
< 0.1%
2711.305 1
< 0.1%

MA for 20 days
Real number (ℝ)

Distinct1992
Distinct (%)99.8%
Missing19
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1510.2945
Minimum822.5425
Maximum2679.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:19.006367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum822.5425
5-th percentile885.09175
Q11000.2987
median1277.8575
Q32080.505
95-th percentile2591.608
Maximum2679.87
Range1857.3275
Interquartile range (IQR)1080.2063

Descriptive statistics

Standard deviation596.07571
Coefficient of variation (CV)0.39467515
Kurtosis-1.0787935
Mean1510.2945
Median Absolute Deviation (MAD)326.0775
Skewness0.66830362
Sum3013037.5
Variance355306.25
MonotonicityNot monotonic
2023-03-15T12:43:19.188763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1274.5875 2
 
0.1%
1084.845 2
 
0.1%
918.56 2
 
0.1%
1537.3525 1
 
< 0.1%
1671.7875 1
 
< 0.1%
1661.4775 1
 
< 0.1%
1652.45 1
 
< 0.1%
1642.6525 1
 
< 0.1%
1634.2325 1
 
< 0.1%
1624.0925 1
 
< 0.1%
Other values (1982) 1982
98.4%
(Missing) 19
 
0.9%
ValueCountFrequency (%)
822.5425 1
< 0.1%
823.49 1
< 0.1%
824.1775 1
< 0.1%
825.4425 1
< 0.1%
826.2625 1
< 0.1%
827.45 1
< 0.1%
829.08 1
< 0.1%
830.83 1
< 0.1%
832.27 1
< 0.1%
836.065 1
< 0.1%
ValueCountFrequency (%)
2679.87 1
< 0.1%
2678.73 1
< 0.1%
2678.46 1
< 0.1%
2677.98 1
< 0.1%
2673.4225 1
< 0.1%
2670.695 1
< 0.1%
2668.5125 1
< 0.1%
2664.83 1
< 0.1%
2664.04 1
< 0.1%
2662.3175 1
< 0.1%

MA for 50 days
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1960
Distinct (%)99.7%
Missing49
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean1507.6801
Minimum865.413
Maximum2637.976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.0 KiB
2023-03-15T12:43:19.372089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum865.413
5-th percentile894.9994
Q1997.42
median1281.859
Q32039.698
95-th percentile2544.609
Maximum2637.976
Range1772.563
Interquartile range (IQR)1042.278

Descriptive statistics

Standard deviation587.59745
Coefficient of variation (CV)0.38973615
Kurtosis-1.0701835
Mean1507.6801
Median Absolute Deviation (MAD)317.169
Skewness0.6864652
Sum2962591.4
Variance345270.76
MonotonicityNot monotonic
2023-03-15T12:43:19.548728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
870.367 2
 
0.1%
934.804 2
 
0.1%
917.169 2
 
0.1%
2495.292 2
 
0.1%
2535.442 2
 
0.1%
1576.498 1
 
< 0.1%
1569.133 1
 
< 0.1%
1561.004 1
 
< 0.1%
1551.386 1
 
< 0.1%
1584.42 1
 
< 0.1%
Other values (1950) 1950
96.8%
(Missing) 49
 
2.4%
ValueCountFrequency (%)
865.413 1
< 0.1%
865.46 1
< 0.1%
865.896 1
< 0.1%
866.086 1
< 0.1%
866.178 1
< 0.1%
866.323 1
< 0.1%
867.268 1
< 0.1%
867.426 1
< 0.1%
867.779 1
< 0.1%
868.609 1
< 0.1%
ValueCountFrequency (%)
2637.976 1
< 0.1%
2637.775 1
< 0.1%
2637.642 1
< 0.1%
2636.451 1
< 0.1%
2636.142 1
< 0.1%
2634.377 1
< 0.1%
2633.656 1
< 0.1%
2632.606 1
< 0.1%
2632.361 1
< 0.1%
2632.136 1
< 0.1%

Daily Return
Real number (ℝ)

Distinct2013
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.00076616971
Minimum-0.50279567
Maximum0.14718041
Zeros1
Zeros (%)< 0.1%
Negative966
Negative (%)48.0%
Memory size96.0 KiB
2023-03-15T12:43:19.729134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.50279567
5-th percentile-0.025294288
Q1-0.009112736
median0.00068062827
Q30.010805013
95-th percentile0.029141456
Maximum0.14718041
Range0.64997608
Interquartile range (IQR)0.019917749

Descriptive statistics

Standard deviation0.021523551
Coefficient of variation (CV)28.092406
Kurtosis152.26177
Mean0.00076616971
Median Absolute Deviation (MAD)0.0099432576
Skewness-6.1330118
Sum1.5422996
Variance0.00046326323
MonotonicityNot monotonic
2023-03-15T12:43:19.924452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02474621347 1
 
< 0.1%
-0.01446038427 1
 
< 0.1%
-0.007530976469 1
 
< 0.1%
0.06250377438 1
 
< 0.1%
0.02510291887 1
 
< 0.1%
-0.00145267973 1
 
< 0.1%
0.001951008021 1
 
< 0.1%
0.0162072004 1
 
< 0.1%
0.03323144957 1
 
< 0.1%
-0.02191266737 1
 
< 0.1%
Other values (2003) 2003
99.5%
ValueCountFrequency (%)
-0.5027956728 1
< 0.1%
-0.1315388772 1
< 0.1%
-0.1234067663 1
< 0.1%
-0.0863483672 1
< 0.1%
-0.08617668532 1
< 0.1%
-0.08106396453 1
< 0.1%
-0.07849681418 1
< 0.1%
-0.07202434843 1
< 0.1%
-0.06874948137 1
< 0.1%
-0.06540432168 1
< 0.1%
ValueCountFrequency (%)
0.1471804113 1
< 0.1%
0.1194022924 1
< 0.1%
0.1096305826 1
< 0.1%
0.1092404925 1
< 0.1%
0.1020325696 1
< 0.1%
0.09693658033 1
< 0.1%
0.08083846863 1
< 0.1%
0.07062715432 1
< 0.1%
0.07048282728 1
< 0.1%
0.06713421187 1
< 0.1%

Interactions

2023-03-15T12:43:08.975739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:07.009084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:09.493229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:12.035608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:14.328763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:18.560513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:22.951680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:27.124792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:33.144458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:37.673232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:42.246354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:50.508838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:55.144227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:00.782963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:03.390584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:06.438868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:09.133750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:07.231130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:09.637930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:12.183996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:14.484594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:19.008600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:23.211975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:27.353880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:33.375613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-15T12:42:11.730388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:14.044206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:17.714738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:22.540020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:26.457575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:32.577508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:37.123081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:41.376594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:49.239416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:54.532733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:00.411632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:03.079421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:05.657233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:08.675540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:11.808594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:09.338697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:11.892170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:14.190775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:18.109652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:22.747945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:26.880418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:32.846513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:37.352976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:41.891043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:50.283592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:42:54.886175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:00.624560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:03.236392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:05.813033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-15T12:43:08.825160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-15T12:43:20.088272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Prev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%DeliverbleMA for 10 daysMA for 20 daysMA for 50 daysDaily Return
Prev Close1.0000.9980.9970.9970.9960.9960.9970.3400.6970.6210.276-0.1970.9910.9780.943-0.037
Open0.9981.0000.9990.9990.9980.9980.9990.3390.6970.6210.275-0.1990.9890.9760.941-0.025
High0.9970.9991.0000.9990.9990.9991.0000.3500.7060.6310.285-0.2060.9890.9760.942-0.000
Low0.9970.9990.9991.0000.9990.9990.9990.3300.6900.6120.266-0.1950.9880.9740.9390.001
Last0.9960.9980.9990.9991.0001.0001.0000.3420.7000.6220.277-0.2030.9880.9750.9400.025
Close0.9960.9980.9990.9991.0001.0001.0000.3420.6990.6220.277-0.2020.9880.9740.9400.024
VWAP0.9970.9991.0000.9991.0001.0001.0000.3410.6990.6220.276-0.2020.9890.9750.9400.007
Volume0.3400.3390.3500.3300.3420.3420.3411.0000.8940.8660.900-0.4160.3380.3390.3380.051
Turnover0.6970.6970.7060.6900.7000.6990.6990.8941.0000.9450.799-0.3880.6950.6930.6870.043
Trades0.6210.6210.6310.6120.6220.6220.6220.8660.9451.0000.776-0.3750.6190.6210.6160.009
Deliverable Volume0.2760.2750.2850.2660.2770.2770.2760.9000.7990.7761.000-0.0320.2760.2820.2850.021
%Deliverble-0.197-0.199-0.206-0.195-0.203-0.202-0.202-0.416-0.388-0.375-0.0321.000-0.189-0.182-0.185-0.109
MA for 10 days0.9910.9890.9890.9880.9880.9880.9890.3380.6950.6190.276-0.1891.0000.9920.957-0.032
MA for 20 days0.9780.9760.9760.9740.9750.9740.9750.3390.6930.6210.282-0.1820.9921.0000.974-0.033
MA for 50 days0.9430.9410.9420.9390.9400.9400.9400.3380.6870.6160.285-0.1850.9570.9741.000-0.034
Daily Return-0.037-0.025-0.0000.0010.0250.0240.0070.0510.0430.0090.021-0.109-0.032-0.033-0.0341.000

Missing values

2023-03-15T12:43:12.090799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-15T12:43:12.610278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-15T12:43:13.012027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%DeliverbleMA for 10 daysMA for 20 daysMA for 50 daysDaily Return
Date
2015-01-01RELIANCEEQ891.15886.3892.00883.65888.00887.90887.626774396.013059e+13162632863770.4227NaNNaNNaNNaN
2015-01-02RELIANCEEQ887.90888.1896.05884.30884.95885.55889.3916758271.490461e+143126910485430.6257NaNNaNNaN-0.002647
2015-01-05RELIANCEEQ885.55885.0890.90874.10875.55875.85881.8823095912.036781e+146703513931320.6032NaNNaNNaN-0.010954
2015-01-06RELIANCEEQ875.85870.0873.00832.00832.50836.10847.6042580433.609133e+1412076723532730.5527NaNNaNNaN-0.045384
2015-01-07RELIANCEEQ836.10837.1858.80837.00858.00854.30849.8147363154.024972e+1412593928854680.6092NaNNaNNaN0.021768
2015-01-08RELIANCEEQ854.30860.1862.90840.15843.00842.05845.7045424573.841574e+147780026638300.5864NaNNaNNaN-0.014339
2015-01-09RELIANCEEQ842.05848.4862.00845.00861.50860.30852.1530613662.608741e+145679516978230.5546NaNNaNNaN0.021673
2015-01-12RELIANCEEQ860.30860.5861.00841.60852.50850.30847.5123087161.956662e+145910312636120.5473NaNNaNNaN-0.011624
2015-01-13RELIANCEEQ850.30853.0854.95840.50845.00843.15844.7722741081.921107e+145075513101960.5761NaNNaNNaN-0.008409
2015-01-14RELIANCEEQ843.15843.0847.00831.20837.00834.95836.0926012862.174913e+148025714687540.5646857.045NaNNaN-0.009725
SymbolSeriesPrev CloseOpenHighLowLastCloseVWAPVolumeTurnoverTradesDeliverable Volume%DeliverbleMA for 10 daysMA for 20 daysMA for 50 daysDaily Return
Date
2023-02-02RELIANCEEQ2339.902318.002348.002311.002325.002326.952324.9163053171.465932e+1527595741536220.65872386.1302446.52002544.870-0.005534
2023-02-03RELIANCEEQ2326.952349.002349.002293.002330.002329.002318.88113988502.643251e+1540648879735550.69952371.8252437.26752539.8680.000881
2023-02-06RELIANCEEQ2329.002315.002321.002305.802312.902311.452311.9268476791.583132e+1529380551916020.75822358.7052425.99502533.745-0.007535
2023-02-07RELIANCEEQ2311.452312.002327.402293.002304.302305.902312.2769850011.615123e+1528134247490780.67992346.2652411.45002525.712-0.002401
2023-02-08RELIANCEEQ2305.902313.002359.902307.002349.652351.952344.9797654902.289982e+1528301867675290.69302339.8652401.12752518.5070.019971
2023-02-09RELIANCEEQ2351.952353.452370.852334.002353.602356.052354.9160792281.431601e+1526409738622830.63532337.2152392.62252511.0010.001743
2023-02-10RELIANCEEQ2356.052354.902354.902321.302336.502336.652331.7749797891.161172e+1518825826341080.52902337.1452385.87502503.268-0.008234
2023-02-13RELIANCEEQ2336.652340.202350.002313.552324.602323.352323.4947776741.110088e+1515176327802050.58192333.5052378.66252495.292-0.005692
2023-02-14RELIANCEEQ2323.352329.952381.902323.652380.002378.102365.5866095641.563542e+1522727740497240.61272335.9302375.36252489.2050.023565
2023-02-15RELIANCEEQ2378.102376.002437.202373.002435.052431.952417.55154619023.737992e+15338567116446330.75312345.1352373.02002484.0400.022644